|Grant Number:||9R01CA165057-05 Interpret this number|
|Primary Investigator:||Kulldorff, Martin|
|Organization:||Harvard Pilgrim Health Care, Inc.|
|Project Title:||SATSCAN: Spatial Scan Statistic Surveillance Software II|
DESCRIPTION (provided by applicant): SaTScan is a free statistical disease surveillance software package implementing the spatial, temporal and spatio-temporal scan statistics. It is used by many scientists and public health officials across the United States and around the world for geographical disease cluster detection and evaluation, and for the early detection of disease outbreaks. With the software, it is possible to determine whether disease cases are randomly distributed over space and/or time, or whether there are statistically significant spatial, temporal and/or spatio-temporal clusters with more (or fewer) cases than expected. Critically, it adjusts for the multiple testing inherent in the many possible cluster locations and sizes evaluated, as well as for covariates. As the number of users increase, there is an increasing number of requests for new SaTScan features and functionalities, including integration with geographical information systems and statistical software packages, graphical output functionalities, power evaluation tools, more general analysis options, and easy to use training material. In this project, we propose to further develop and maintain the SaTScan software to fulfill many of these needs. PUBLIC HEALTH RELEVANCE: The SaTScan disease surveillance software has around 13,000 registered users, including over 800 at federal (CDC), state and local public health departments across the United States. Additional features, functionalities and training material will enable users to apply the software in new and innovative ways, as well as more efficiently.
Comments on 'A critical look at prospective surveillance using a scan statistic' by T. Correa, M. Costa, and R. Assunção.
Authors: Kulldorff M, Kleinman K
Source: Stat Med, 2015 Mar 30;34(7), p. 1094-5.
Relative risk estimates from spatial and space-time scan statistics: are they biased?
Authors: Prates MO, Kulldorff M, Assunção RM
Source: Stat Med, 2014 Jul 10;33(15), p. 2634-44.
EPub date: 2014 Mar 18.
Influence of spatial resolution on space-time disease cluster detection.
Authors: Jones SG, Kulldorff M
Source: PLoS One, 2012;7(10), p. e48036.
EPub date: 2012 Oct 24.